000			02844nam a2200325 i 4500
999			_c399517 _d399517
001			399517
003			ES-MaUEC
005			20240427143115.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230325s2023 sz | fo |||| 0|eng d
020			_a9783031229596
024	7		_a10.1007/978-3-031-22959-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aR858 _b2023 EB
245	0	0	_aImage Based Computing for Food and Health Analytics : _bRequirements, Challenges, Solutions and Practices : IBCFHA _cedited by Rajeev Tiwari, Deepika Koundal, Shuchi Upadhyay
250			_a1st ed. 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_a1. Food Computing Research opportunities using AI and M -- Estimating the Risk of Diabetes Using Association Rule Mining Based on Clustering -- Digital Twins for Food Nutrition and Health Based on Cloud Communication -- Smart Healthcare Systems: An IoT with Fog Computing based Solution for Healthcare,- An Intelligent and Secure Real-time Environment Monitoring System for healthcare using IoT and Cloud Computing with the Mobile Application Support -- Efficient BREV Ensemble Framework: A Case Study of Breast Cancer Prediction,- Current and Future Trends of Cloud-based solutions for Healthcare,- Secure Authentication in IoT based healthcare management environment using integrated Fog computing enabled blockchain system -- SENTIMENT ANALYSIS OF COVID-19 TWEETS USING VOTING ENSEMBLE-BASED MODEL -- Cloud and machine learning based solutions for healthcare and preventio -- Interoperable Cloud-Fog architecture in IoT-enabled Health Sector -- COVID-19 Wireless Self-Assessment Software for Rural Areas in Nigeria -- Efficient Fog-to-Cloud Internet-of-Medical-Things System.
520			_aImage Based Computing for Food and Health Analytics covers the current status of food image analysis and presents computer vision and image processing based solutions to enhance and improve the accuracy of current measurements of dietary intake. Many solutions are presented to improve the accuracy of assessment by analyzing health images, data and food industry based images captured by mobile devices. Key technique innovations based on Artificial Intelligence and deep learning-based food image recognition algorithms are also discussed.
988			_aSpringer_BiomedLife_2023
650		7	_2embne _9421154 _aInformática médica
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-22959-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2024 _dz _eb _zSI